A Label-Free Platform for Identification of Exosomes from Different Sources

微泡 鉴定(生物学) 计算生物学 计算机科学 化学 医学 生物 小RNA 生物化学 植物 基因
作者
Zhongbo Yan,Suman Dutta,Zirui Liu,Xinke Yu,Neda Mesgarzadeh,Feng Ji,Gal Bitan,Ya‐Hong Xie
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:4 (2): 488-497 被引量:140
标识
DOI:10.1021/acssensors.8b01564
摘要

Exosomes contain cell- and cell-state-specific cargos of proteins, lipids, and nucleic acids and play significant roles in cell signaling and cell-cell communication. Current research into exosome-based biomarkers has relied largely on analyzing candidate biomarkers, i.e., specific proteins or nucleic acids. However, this approach may miss important biomarkers that are yet to be identified. Alternative approaches are to analyze the entire exosome system, either by "omics" methods or by techniques that provide "fingerprints" of the system without identifying each individual biomolecule component. Here, we describe a platform of the latter type, which is based on surface-enhanced Raman spectroscopy (SERS) in combination with multivariate analysis, and demonstrate the utility of this platform for analyzing exosomes derived from different biological sources. First, we examined whether this analysis could use exosomes isolated from fetal bovine serum using a simple, commercially available isolation kit or necessitates the higher purity achieved by the "gold standard" ultracentrifugation/filtration procedure. Our data demonstrate that the latter method is required for this type of analysis. Having established this requirement, we rigorously analyzed the Raman spectral signature of individual exosomes using a unique, hybrid SERS substrate made of a graphene-covered Au surface containing a quasi-periodic array of pyramids. To examine the source of the Raman signal, we used Raman mapping of low and high spatial resolution combined with morphological identification of exosomes by scanning electron microscopy. Both approaches suggested that the spectra were collected from single exosomes. Finally, we demonstrate for the first time that our platform can distinguish among exosomes from different biological sources based on their Raman signature, a promising approach for developing exosome-based fingerprinting. Our study serves as a solid technological foundation for future exploration of the roles of exosomes in various biological processes and their use as biomarkers for disease diagnosis and treatment monitoring.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
5555完成签到,获得积分10
1秒前
Waris完成签到 ,获得积分10
1秒前
伶俐盼兰发布了新的文献求助10
2秒前
Xiu完成签到,获得积分10
3秒前
苏喜财应助曾建采纳,获得10
3秒前
3秒前
4秒前
perper发布了新的文献求助10
4秒前
Akim应助lulu采纳,获得10
4秒前
yueguang完成签到,获得积分10
6秒前
6秒前
阮逸君完成签到,获得积分10
6秒前
量子星尘发布了新的文献求助10
8秒前
宓函完成签到,获得积分10
9秒前
9秒前
9秒前
学渣小林发布了新的文献求助10
11秒前
12秒前
reai关注了科研通微信公众号
12秒前
Nizarn发布了新的文献求助30
13秒前
小蘑菇应助sxh采纳,获得10
15秒前
15秒前
量子星尘发布了新的文献求助10
16秒前
16秒前
17秒前
17秒前
科研通AI6应助科研通管家采纳,获得10
18秒前
科研通AI6应助科研通管家采纳,获得10
18秒前
Zero发布了新的文献求助10
18秒前
沉默小虾米完成签到 ,获得积分10
18秒前
18秒前
在水一方应助科研通管家采纳,获得10
18秒前
科研通AI6应助科研通管家采纳,获得10
18秒前
19秒前
科研通AI6应助科研通管家采纳,获得10
19秒前
科目三应助科研通管家采纳,获得10
19秒前
Ava应助科研通管家采纳,获得10
19秒前
科研通AI6应助科研通管家采纳,获得10
19秒前
丘比特应助科研通管家采纳,获得30
19秒前
吼吼应助科研通管家采纳,获得10
20秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 9000
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Real World Research, 5th Edition 680
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 660
Superabsorbent Polymers 600
Handbook of Migration, International Relations and Security in Asia 555
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5679489
求助须知:如何正确求助?哪些是违规求助? 4990946
关于积分的说明 15169676
捐赠科研通 4839270
什么是DOI,文献DOI怎么找? 2593233
邀请新用户注册赠送积分活动 1546348
关于科研通互助平台的介绍 1504472